Search Results (88)

A methodology for deriving high-resolution (5-km) surface shortwave radiative (SWR) fluxes over the Arctic was applied to observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) during the spring and summer melt season (March–September) of 2007, when the Arctic experienced a historically significant and well-documented decline in sea ice extent. The derived SWR fluxes were used to estimate solar heat input into the Arctic Ocean during the melt season, a task that had not previously been undertaken at such high spatial resolution. According to the National Snow and Ice Data Center (NSIDC), Arctic sea ice extent reached a record minimum of 4.13 million km² on 16 September 2007, approximately 38% below the 1979–2000 climatological mean and 24% below the previous record minimum in 2005. This extreme reduction in sea ice resulted in several weeks of ice-free opening along portions of the ‘Northwest Passage’. Availability of high spatial resolution SWR fluxes in the Arctic is particularly important for improving estimates of solar heat input into the Arctic Ocean, especially within the highly heterogeneous marginal ice zone. To facilitate comparison with sea ice concentration products from NSIDC, the MODIS-derived 5-km SWR fluxes were aggregated to 0.25° equal-area grid cells (approximately 25 km resolution). Our results show that the abrupt increase in the open water fraction produced anomalies in solar heating to the upper ocean exceeding 300%, hereby enhancing the ice–albedo feedback mechanism and promoting further sea ice melt. The estimated monthly cumulative solar heat input to the ocean for a nominal 1° grid cell was 164.9 MJ m⁻² in May. In contrast, the corresponding four 0.25° sub-grid cells, resolved using the high-resolution MODIS data, exhibited cumulative heat inputs of 58.0, 93.0, 189.3, and 296.4 MJ m⁻², respectively. Although the average heat input for the 1° grid cell (165 MJ m⁻² was similar to the average value obtained from the four 0.25° grid cells (159 MJ m⁻² the substantial sub-grid variability is important because the oceanic and sea-ice responses to solar heating are highly nonlinear. Consequently, unresolved spatial variability can significantly affect the magnitude of derived quantities and associated feedback processes. These findings demonstrate the importance of high-spatial-resolution radiative flux information for accurately quantifying ocean heating and ice–ocean interactions in the Arctic. Full article

The “New Arctic” regime represents a prominent climatic feature of the Arctic Ocean under global warming, characterized by persistently low summer sea ice extent, a marked reduction in sea ice thickness, and an expansion of open water areas at high latitudes. As a key indicator of the Arctic sea ice system, the spatiotemporal evolution of sea ice thickness and its underlying driving mechanisms remain incompletely understood. Using reanalysis datasets and remote sensing observations, this study identifies major abrupt shifts in Arctic sea ice thickness under the New Arctic regime, reveals the spatiotemporal distribution characteristics of winter sea ice thickness, and examines the driving factors from both thermodynamic and dynamic perspectives. The results show that the evolution of Arctic sea ice thickness can be divided into three phases: a high-level period during the “Traditional Arctic” (1979–1992), a rapid thinning period during the New Arctic transition (1993–2012), and a low-level stabilization period in the New Arctic regime (2013–2023). The first EOF mode of winter sea ice thickness depicts a spatially consistent thinning pattern across the entire Arctic, with the most significant reduction occurring in the multi-year ice regions north of the Canadian Arctic Archipelago and Greenland. The second EOF mode exhibits an out-of-phase variation between the Atlantic and Pacific sectors of the Arctic, accompanied by a shrinking amplitude and weakened regional oscillations. The coupling between surface air temperature and sea ice thickness displays distinct phase dependence: their negative correlation is strongest during the transition period (r = −0.78, p < 0.001) but becomes statistically insignificant in the New Arctic regime. Sea ice motion speed exhibits an overall accelerating trend, which extends from the marginal seasonal ice zones toward the high-latitude multi-year ice regions, accompanied by a notably enhanced sensitivity of sea ice motion to wind forcing. Sea ice volume flux through the Fram Strait is primarily controlled by ice motion speed, whose contribution to the flux is approximately 2.6 times that of ice thickness. The recovery of ice drift speed offsets the thinning of sea ice cover, leading to a partial rebound in volume flux during the New Arctic steady state. This study identifies the evolutionary patterns and drivers of Arctic sea ice thickness under the New Arctic regime, providing a scientific basis for further understanding the changes in the Arctic climate system and associated air–sea ice interactions. Full article

Three-dimensional (3D) glacier velocities capture the full dynamic behavior of ice masses. For marine-terminating glaciers, acquiring 3D velocity fields is particularly critical for quantifying ice discharge into the ocean, assessing the stability of floating ice tongues, and constraining ice–ocean interactions that govern submarine melting, calving processes, and freshwater fluxes to the ocean. To further investigate glacier dynamics and elucidate ice–ocean interaction mechanisms, this study analyzed the 3D velocity of the Petermann Glacier throughout 2021 using long-term Sentinel-1 synthetic aperture radar (SAR) observations. First, two-dimensional velocity time series were derived from ascending and descending SAR images, and the glacier’s 3D velocity components were reconstructed based on the geometric relationships between the two viewing geometries. The estimated 3D velocities were then used as prior constraints, and glacier motion was treated as a continuously evolving state variable within a Kalman filtering framework. Multi-track, asynchronous remote sensing observations were integrated into a unified system to obtain a stable and temporally continuous 3D velocity field. Finally, statistical analyses of the 3D velocity time series were conducted to characterize spatiotemporal variations, seasonal patterns, and topographic influences on glacier motion, thereby providing quantitative insights into the dynamic coupling between glacier and ocean. Full article

Polar sea ice is undergoing rapid change, with recent record-low extents in both hemispheres, raising the demand for skillful predictions from days to seasons for navigation, ecosystem management, and climate risk assessment. Accurate sea ice prediction is essential for understanding coupled climate processes, supporting safe polar operations, and informing adaptation strategies. Physics-based numerical models remain the backbone of operational forecasting, but their skill is limited by uncertainties in coupled ocean–ice–atmosphere processes, parameterizations, and sparse observations, especially in the marginal ice zone and during melt seasons. Statistical and empirical models can provide useful baselines for low-dimensional indices or short lead times, yet they often struggle to represent high-dimensional, nonlinear interactions and regime shifts. This review synthesizes recent progress of DL for key sea ice prediction targets, including sea ice concentration/extent, thickness, and motion, and organizes methods into (i) sequential architectures (e.g., LSTM/GRU and temporal Transformers) for temporal dependencies, (ii) image-to-image and vision models (e.g., CNN/U-Net, vision Transformers, and diffusion or GAN-based generators) for spatial structures and downscaling, and (iii) spatiotemporal fusion frameworks that jointly model space–time dynamics. We further summarize hybrid strategies that integrate DL with numerical models through post-processing, emulation, and data assimilation, as well as physics-informed learning that embeds conservation laws or dynamical constraints. Despite rapid advances, challenges remain in generalization under non-stationary climate conditions, dataset shift, and physical consistency (e.g., mass/energy conservation), interpretability, and fair evaluation across regions and lead times. We conclude with practical recommendations for future research, including standardized benchmarks, uncertainty-aware probabilistic forecasting, physics-guided training and neural operators for long-range dynamics, and foundation models that leverage self-supervised pretraining on large-scale Earth observation archives. Full article

A theoretical model of the interaction between a following current and a semi-infinite floating ice sheet under compressive stress near a vertical impermeable wall is developed, within the scope of linear water wave theory, to study the hydroelastic behavior. The conceptual framework defining the buoyant ice structure incorporates the tenets of elastic beam theory. The associated fluid dynamics are governed by strict adherence to the potential flow paradigm. To resolve the undetermined parameters appearing in the Fourier series decomposition of the potential functions, investigators systematically apply higher-order criteria detailing the coupling relationships between modes. The current results are compared with a specific case of results available in the literature, and the convergence analysis of the analytical solution is made for computational accuracy. Further, the free edge conditions are applied at the edge of the floating ice sheet, and the effects of current speed, compressive stress, the thickness of the ice sheet, flexural rigidity, water depth on the strain, displacements, reflection wave amplitude, and the horizontal force on the rigid vertical wall are analyzed in detail. It is found that the higher values of the following current heighten the strain, displacements, reflection amplitude, and force on the wall. The study’s outcomes are considered to benefit not just cold region design applications but also the engineering of resilient floating structures for oceanic and offshore environments, and to the design of marine structures. Full article

The decline in Arctic summer sea ice alters air–sea gas exchange. Because the Arctic Ocean accounts for 5%–14% of global oceanic carbon uptake, understanding how sea ice melt impacts the ocean’s carbon sink capacity is central to constraining future fluxes. In this study, we focus on Young Sound-Tyrolerfjord in Northeast Greenland to examine the sea ice−ocean interaction during the transition from melt onset to melt pond drainage. High-frequency measurements of partial pressure of CO₂ (pCO₂) and seawater physical properties were taken 2.5 m below the sea ice. Our results reveal that pCO₂ in the seawater was undersaturated (248–354 μatm) compared to the atmosphere (401 μatm), showing that the seawater has the potential to take up atmospheric CO₂ as the sea ice breaks up. The pCO₂ undersaturation was attributed to dilution resulting from mixing meltwater from snow and sea ice with the under-ice seawater. Additionally, the drainage of melt pond water that had been in contact with the atmosphere into the under-ice seawater further lowered pCO₂. Melt pond drainage represents an initial connection between the atmosphere and under-ice seawater through meter-thick sea ice during the summer thaw. Our study demonstrates that snow and sea ice melt reduce pCO₂ in under-ice seawater, enhancing its potential for atmospheric CO₂ uptake during sea ice breakup. Full article

The ongoing global warming threatens the Greenland Ice Sheet (GIS), which has exhibited an overall mass loss since 1990. This loss varies annually and interannually, reflecting the intricate interactions between the ice sheet and atmospheric and oceanic circulations. We investigate GIS mass balance variations (2002–2024) using data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions. Monthly mass anomalies from the International Combination Service for Time-variable Gravity Fields (COST-G) solution are compared with cumulative climate indices (North Atlantic Oscillation—NAO, Greenland Blocking Index—GBI, Atlantic Multidecadal Oscillation—AMO) and meteorological parameters (temperature, precipitation, surface albedo). Empirical Orthogonal Function analysis reveals five principal modes of variations, the first capturing annual and interannual frequencies (4–7 and 11 years), while subsequent modes only describe interannual frequencies. Wavelet analysis shows significant annual correlations between GIS mass changes and temperature (r = −0.88), NAO (r = 0.74), and GBI (r = −0.85). An annual cycle connects GIS mass changes, climatic indices, and meteorological parameters, while interannual variations highlight the role of the AMO and the NAO. The presence of an 11-year periodicity with the mass variations for NAO, GBI, and temperature strongly correlates with solar activity. Full article

Understanding global precipitation trends is critical for managing water resources, anticipating extreme events, and assessing the impacts of climate change. This study analyzes spatial and temporal patterns of precipitation from 1998 to 2024 using NASA’s Global Precipitation Measurement Mission (GPM) Integrated Multi-satellite Retrievals for (IMERG) Version 7, which merges satellite observations with rain-gauge data at 0.1° resolution. A total of 324 monthly datasets were aggregated into annual and seasonal composites to evaluate annual and seasonal trends in global precipitation. The non-parametric Mann–Kendall test was applied at the pixel scale to detect statistically significant monotonic trends, and Sen’s slope estimator method was used to quantify the magnitude of change in mean annual and seasonal global precipitation. Results reveal robust and geographically consistent patterns: significant wetting trends are evident in high-latitude regions, with the Arctic and Southern Oceans showing the strongest increases across multiple seasons, including +0.04 mm/day in December–January–February for the Arctic Ocean and +0.04 mm/day in June–July–August for the Southern Ocean. Northern China also demonstrates persistent increases, aligned with recent intensification of extreme late-season precipitation. In contrast, significant drying trends are detected in the tropical East Pacific (up to −0.02 mm/day), northern South America, and some areas in central-southern Africa, highlighting regions at risk of sustained hydroclimatic stress. The North Atlantic south of Greenland emerges as a summer drying hotspot, consistent with Greenland Ice Sheet melt enhancing stratification and reducing precipitation. Collectively, the findings underscore a dual pattern of wetting at high latitudes and drying in tropical belts, emphasizing the role of polar amplification, ocean–atmosphere interactions, and climate variability in shaping Earth’s precipitation dynamics. Full article

The study focuses on the detection of breaking wave crests in the highly dynamic waters of an Antarctic coastal polynya using high-resolution panchromatic satellite imagery. Accurate assessment of whitecap coverage is crucial for improving our understanding of the interactions between wave generation, air–sea heat exchange, and sea ice formation in these complex environments. As open-ocean whitecap detection methods are inadequate in coastal polynyas partially covered with frazil ice, we discuss an approach that exploits specific lighting conditions: the alignment of sunlight with the dominant wind direction and low solar elevation. Under such conditions, steep breaking waves cast pronounced shadows, which are used as the primary indicator of wave crests, particularly in frazil streak zones. The algorithm is optimized to exploit these conditions and minimize false positives along frazil streak boundaries. We applied the algorithm to a WorldView-2 image covering different parts of Terra Nova Bay Polynya (Ross Sea), a dynamic polar coastal zone. This case study demonstrates that the spatial distribution of detected breaking waves is consistent with ice conditions and wind forcing patterns, while also revealing deviations that point to complex wind–wave–ice interactions. Although quantitative validation of satellite-derived whitecaps coverage was not possible due to the lack of in situ data, the method performs reliably under a range of conditions. Limitations of the proposed approach are pointed out and discussed. Finally, the study highlights the risk of misinterpretation of lower-resolution reflectance data in areas where whitecaps and sea ice coexist at subpixel scales. Full article

Sea ice is highly dynamic. Differences in the sea ice drift velocity and direction can cause deformations such as ridges and rubble fields or open up leads. These and other deformations have a major impact on the interaction between the atmosphere, sea ice and the ocean, and strongly influence ship navigability in polar waters. Spaceborne Synthetic Aperture Radar (SAR) data is well suited to observing the sea ice and retrieving sea ice drift vector fields at a small scale (<1 km), revealing deformation zones. This paper introduces a software processor designed to retrieve high-resolution sea ice drift vector fields from pairs of subsequent SAR acquisitions using phase correlation embedded in a multiscale Gaussian image pyramid. We assess the accuracy of the algorithm by using drift buoys and landfast ice boundaries manually outlined from large series of TerraSAR-X acquisitions taken during winter and spring sea ice break up. In particular, we provide a first analysis of the localization accuracy in deformation zones. Overall, our experiments show that deformation zones are well detected, but can be misplaced by up to 1.1 km. An additional interferometric analysis narrows down the location of the landfast ice boundary. Full article

As economically important sympatric species in the Northwest Pacific, the Japanese sardine (Sardinops melanostictus) and Chub mackerel (Scomber japonicus) exhibit significant biological interactions. Understanding the impact of interspecies competition on their habitat dynamics can provide crucial insights for the sustainable development and management of these interconnected species resources. This study utilizes fisheries data of S. melanostictus and S. japonicus from the Northwest Pacific, collected from June to November between 2017 and 2020. We integrated various environmental parameters, including temperature at different depths (0, 50, 100, 150, and 200 m), eddy kinetic energy (EKE), sea surface height (SSH), chlorophyll-a concentration (Chl-a), and the oceanic Niño index (ONI), to construct interspecific competition species distribution model (icSDM) for both species. We validated these models by overlaying the predicted habitats with fisheries data from 2021 and performing cross-validation to assess the models’ reliability. Furthermore, we conducted correlation analyses of the habitats of these two species to evaluate the impact of interspecies relationships on their habitat dynamics. The results indicate that, compared to single-species habitat models, the interspecific competition species distribution model (icSDM) for these two species exhibit a significantly higher explanatory power, with R² values increasing by up to 0.29; interspecific competition significantly influences the habitat dynamics of S. melanostictus and S. japonicus, strengthening the correlation between their habitat changes. This relationship exhibits a positive correlation at specific stages, with the highest correlations observed in June, July, and October, at 0.81, 0.80, and 0.88, respectively; interspecific competition also demonstrates stage-specific differences in its impact on the habitat dynamics of S. melanostictus and S. japonicus, with the most pronounced differences occurring in August and November. Compared to S. melanostictus, interspecific competition is more beneficial for the expansion of the optimal habitat (HIS ≥ 0.6) for S. japonicus and, to some extent, inhibits the habitat expansion of S. melanostictus. The variation in migratory routes and predatory interactions (with larger individuals of S. japonicus preying on smaller individuals of S. melanostictus) likely constitutes the primary factors contributing to these observed differences. Full article

Oceanic mesoscale activity influences the atmosphere in the southwestern and southern sectors of the Atlantic Ocean. However, the influence of high latitudes, specifically sea ice, on mid-latitudes and a better understanding of mesoscale ocean–atmosphere thermodynamic interactions still require further study. To quantify the effects of oceanic mesoscale activity during the periods of maximum and minimum Antarctic sea ice extent (September 2019 and February 2020), numerical experiments were conducted using a coupled regional model and an online two-dimensional spatial filter to remove high-frequency sea surface temperature (SST) oscillations. The largest SST anomalies were observed in the Brazil–Malvinas Confluence and along oceanic fronts in September, with maximum SST anomalies reaching 4.23 °C and −3.71 °C. In February, the anomalies were 2.18 °C and −3.06 °C. The influence of oceanic mesoscale activity was evident in surface atmospheric variables, with larger anomalies also observed in September. This influence led to changes in the vertical structure of the atmosphere, affecting the development of the marine atmospheric boundary layer (MABL) and influencing the free atmosphere above the MABL. Modulations in precipitation patterns were observed, not only in oceanic regions, but also in adjacent continental areas. This research provides a novel perspective on ocean–atmosphere thermodynamic coupling, highlighting the mesoscale role and importance of its representation in the study region. Full article

A comprehensive diagnosis of eddy–mean flow interaction in the Kuroshio Current (KC) region and the associated energy conversion pathway is conducted employing a state-of-the-art high-resolution global ocean–sea ice coupled model. The spatial distributions of the energy reservoirs and their conversions exhibit significant complexity. The cross-stream variation is found in the energy conversion pattern in the along-coast region, whereas a mixed positive–negative conversion pattern is observed in the off-coast region. Considering the area-integrated conversion rates between energy reservoirs, barotropic and baroclinic instabilities dominate the energy transferring from the mean flow to eddy field in the KC region. When the KC separates from the coast, it becomes highly unstable and the energy conversion rates intensify visibly; moreover, the local variations of the energy conversion are significantly influenced by the topography in the KC extension region. The mean available potential energy is the total energetic source to drive the barotropic and baroclinic energy pathway in the whole KC region, while the mean kinetic energy supplies the total energy in the extension region. For the whole KC region, the mean current transfers 84.9 GW of kinetic energy and 37.3 GW of available potential energy to the eddy field. The eddy kinetic energy is generated by mixed barotropic and baroclinic processes, amounting to 84.9 GW and 15.03 GW, respectively, indicating that topography dominates the generation of mesoscale eddy. Mean kinetic energy amounts to 11.08 GW of power from the mean available potential energy and subsequently supplies the barotropic pathway. Full article

This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite ICESat-2’s design primarily as an altimetry mission with a single-wavelength, low-power, high-repetition-rate laser, ICESat-2 effectively captures global aerosol distribution patterns and can provide valuable insights to bridge the observational gap between the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) missions to support future spaceborne lidar mission design. The machine learning approach outperforms traditional thresholding methods, particularly in complex conditions of cloud embedded in aerosol, owing to a finer spatiotemporal resolution. Our results show that annually, between 60°S and 60°N, 78.4%, 17.0%, and 4.5% of aerosols are located within the 0–2 km, 2–4 km, and 4–6 km altitude ranges, respectively. Regional analyses cover the Arabian Sea (ARS), Arabian Peninsula (ARP), South Asia (SAS), East Asia (EAS), Southeast Asia (SEA), the Americas, and tropical oceans. Vertical aerosol structures reveal strong trans-Atlantic dust transport from the Sahara in summer and biomass burning smoke transport from the Savanna during dry seasons. Marine aerosol belts are most prominent in the tropics, contrasting with earlier reports of the Southern Ocean maxima. This work highlights the importance of vertical aerosol distributions needed for more accurate quantification of the aerosol–cloud interaction influence on radiative forcing for improving global climate models. Full article

As fundamental geophysical information, the high-precision detection of shallow water bathymetry is critical data support for the utilization of island resources and coral reef protection delimitation. In recent years, the combination of active and passive remote sensing technologies has led to a revolutionary breakthrough in satellite-derived bathymetry (SDB). Optical SDB extracts bathymetry by quantifying light–water–bottom interactions. Therefore, the apparent differences in the reflectance of different bottom types in specific wavelength bands are a core component of SDB. In this study, refined classification was performed for complex seafloor sediment and geomorphic features in coral reef habitats. A multi-model synergistic SDB fusion approach constrained by coral reef habitat classification based on the deep learning framework Mamba was constructed. The dual error of the global single model was suppressed by exploiting sediment and geomorphic partitions, as well as the accuracy complementarity of different models. Based on multispectral remote sensing imagery Sentinel-2 and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) active spaceborne lidar bathymetry data, wide-range and high-accuracy coral reef habitat classification results and bathymetry information were obtained for the Yuya Shoal (0–23 m) and Niihau Island (0–40 m). The results showed that the overall Mean Absolute Errors (MAEs) in the two study areas were 0.2 m and 0.5 m and the Mean Absolute Percentage Errors (MAPEs) were 9.77% and 6.47%, respectively. And R² reached 0.98 in both areas. The estimated error of the SDB fusion strategy based on coral reef habitat classification was reduced by more than 90% compared with classical SDB models and a single machine learning method, thereby improving the capability of SDB in complex geomorphic ocean areas. Full article